The field of protein inverse folding is witnessing significant advancements with the development of innovative models and techniques. Researchers are focusing on improving the accuracy and robustness of these models, particularly in terms of energy-driven predictions. Recent studies have highlighted the importance of understanding the free-energy foundations of inverse folding models and addressing subtle systematic biases in predicted structures. Meanwhile, in the area of visual servoing control, new algorithms and control strategies are being proposed to enhance the precision and robustness of camera-based systems in automated manufacturing environments. Noteworthy papers in these areas include the introduction of a Debiasing Structure AutoEncoder to improve inverse folding performance, the development of EnerBridge-DPO for energy-guided protein inverse folding, and the proposal of a feedforward Youla Parameterization Method for avoiding local minima in stereo image-based visual servoing control. These innovative approaches demonstrate the potential for significant improvements in protein design and visual servoing control, and are expected to have a lasting impact on the field.